Retrieval-Augmented Generation (RAG) is an AI architecture pattern that enhances LLM responses by first retrieving relevant documents from an external knowledge base, then using those documents as context when generating answers. RAG solves key LLM limitations including hallucination, outdated knowledge, and lack of domain expertise. The retrieval step typically uses vector embeddings and similarity search to find relevant passages, which are then injected into the LLM prompt. RAG is widely used in enterprise AI applications where accuracy and up-to-date information are critical.
Frequently Asked Questions
What is RAG in AI?
RAG (Retrieval-Augmented Generation) is a technique that improves AI responses by retrieving relevant documents from a knowledge base before generating an answer, reducing hallucinations and enabling access to current information.
Why is RAG important?
RAG grounds LLM responses in real data, dramatically reducing hallucinations and enabling AI systems to access proprietary or up-to-date information without retraining.